CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

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Computer-aided design (CAD) files are used to create digital designs for various structures – from the smallest chips in the high-tech industry to large-scale buildings and bridges in the civil engineering space. We found that most exploits and malicious payloads are deployed through Auto List Processing (AutoLISP) source code (LSP) or Fast Load AutoLISP (FAS) files, which are non-executable files (NEFs) containing scripts in the AutoLISP language that are native to AutoCAD; While antivirus software is capable of detecting many malicious CAD files, the potential to improve protection by using a dedicated machine learning (ML) based detection solution remains, especially against unknown and sophisticated CAD malware. In this study, we are the first to propose designated feature extraction methods and a robust framework aimed at the detection of known and unknown AutoLISP malware using ML algorithms. To accomplish this, we examined the structure, functionality, and ecosystems of AutoLISP files and collected the largest known representative collection of LSP files consisting of 6418 malicious and benign files (labeled and verified). We then explored the use of two novel static-analysis-based feature extraction methods (knowledge-based and structural) designated for LSP files to extract a discriminative set of informative features, which can subsequently be used by ML models to detect malicious LSP files. These two feature extraction methods serve as the basis of the proposed detection framework, whose performance we comprehensively compare to both widely used antiviruses and baseline ML models based on existing feature extraction methods, including MinHash, Bidirectional Encoder Representations from Transformers (BERT), and n-gram. Our results highlight our methods' contributions to the detection of unknown AutoLISP malware and demonstrate their ability to outperform existing methods. The best performance in the task of unknown malicious LSP file detection was obtained by the Artificial Neural Networks (ANN) model trained on 100 knowledge-based features, which obtained a true positive rate (TPR) of 99.49% with a false positive rate (FPR) of 0.57%. Our framework's role in explainability is also highlighted, as we also present the prominent features that contribute most to the model's detection capabilities; this information can be used for explainability purposes. We conclude by evaluating the proposed framework's ability to detect a malicious file from an unknown AutoLISP malware family and by evaluating our framework on an additional independent test set that originated from another source, scenarios that are often faced by malware detection solutions.